DESIGN AND IMPLEMENTATION OF PERFORMANCE MEASUREMENT SYSTEM IN AN INTERNATIONAL SURFWEAR COMPANY Y. DUCQ(1), H. KROMM(2) 1 IMS-LAPS/GRAI, University of Bordeaux, UMR 5218 CNRS 351 Cours de la Libération, 33405 Talence, France,
[email protected] 2 ACTHAN EXPERTISES – 18 route de Beychac – 33750 Saint Germain de Puch -
[email protected] Abstract This paper aims to present an application of ECOGRAI method to a surfwear company. This application is specific in the sense that the company has no factory and subcontract its whole production. ECOGRAI is based on enterprise modelling techniques and aims to identify and implement performance indicators. After a presentation of the industrial context insisting on the reasons of changes, ECOGRAI will be slightly presented. Then the results of the study will be detailed insisting on the models and on some key performance indicators. This approach allows to have a performance indicators system implemented from the strategic to the operational levels, for all the enterprise functions. Moreover, it allows also to have a strong appropriateness of the performance indicators by production managers and to develop a common language in the enterprise. Key words: Performance Measurement Case Study, ECOGRAI, Sportwear company
Introduction The domain of Performance Measurement has been investigated for more than twenty years leading to a lot of methods around the world, developed either by researchers or more pragmatically by practitioners, in order to define and implement indicators (Franco 2003 et al)(Ravelomanantsoa 2006 et al). All these methods have been developed independently based on system theory, production management theory or accounting methods, according to the background of developers. Among all these thirty methods, more or less used and well known, one can cite the famous ones or the most used or disseminated around the world as Balanced Score Card (BSC) (Kaplan, 1996), the Performance Prism (Neely 2002), ECOGRAI (Ducq 2005), QMPMS (Bititci 1997), or IDPMS (Ghalayini 1997). For instance, the main principle of BSC is to ensure a deployment of the strategy through the whole enterprise building a strategic map. So, several perspectives are proposed: financial, customer, internal business process, innovation and learning. For each perspective, objectives and performance indicators are defined. We can also mention the Performance Prism method (Neely 2002 et al). This method is based on measures for managing stakeholders (investors, customers, intermediaries, employees, suppliers, regulators, communities) and their impact on performance. After having
determined the main stakeholders, five points of view are followed: expectations, strategy, business processes, capabilities and expectations. The advantage of this method is to allow to link external performances expected by stakeholders and internal performances of the enterprise. This is a strong advantage of Performance Prism in comparison with BSC. The second advantage of this method is to have measures aligned with cross-functional “end to end” business processes. Nevertheless, the action means to achieve the objectives are not proposed and the decomposition of objectives and performance indicators inside the company is not systematically studied. This could avoid having conflicts between stakeholders’ expectations and capabilities implemented to reach them. The QMPMS (Quantitative Model for Performance Measurement System) is a system elaborated for the modelling and the understanding of relationships between quantitative measures. The process includes three main steps, using several tools as cognitive maps for identification of performance factors in the first step. Then, cause and effects diagrams are built for the hierarchy of factors in the second step and then AHP (Analytical Hierarchical Process) of Saaty is used in the third step for the quantification of factors. IDPMS (Integrated Dynamic Performance Measurement System) system includes three main domains of evaluation: the first one is the management domain which includes the performance of the management of the various functions. The second domain concerns the domain of teams involved in the continuous improvement. The last domain concerns the domain of industrial workshop involved in the manufacturing of products. However, most of case studies presented within these methods are related to typical industries (Ducq 2003 et al.). Various kinds of industrial organisations were concerned from the single enterprise to the complex supply chains. The domain of “fashion products” company is not very explored in particular because most of decisions are made based on the experience of managers, experience of fashion/style expectations, of customer reactions on samples...The second reason is that one collection is always different to the other and this is very difficult to decide based on results of previous collections, this type of market being very versatile and aggressive. In this paper, ECOGRAI method was chosen by the consultants with the agreement of the company even if BSC was already known by both and previously used by the company. This paper aims to show a case study of design, implementation and impact of a performance measurement system in the domain of surfwear products, in particular dedicated to young people. So, in a first part, the context of the study will be presented insisting on the originalities of this company. In a second time ECOGRAI will be presented in its last version. Then, the results of the application will be detailed insisting also on the general planning. In conclusion, authors will insist on the perspectives of this study.
The enterprise context Description of the company SURFWEAR Company was created forty years ago. This company design and distribute products on its brand related to slipping and aquatic sports. The company is composed of four main entities: one in Australia, one in Brazil, one in US and one in Europe. This is this last entity which was studied. Each of these entities design a part of the worldwide collection in addition to the collection related to its local market.
The main products of the brand are: - Slipping sport equipments: surfboards, snowboards, skies... - Surfwear: shorts, swimming shorts, tee-shirts, trousers, shirts, sweat shirts... - Wetsuits in neoprene or lycra, - Footwear: shoes for men, women, children - Eyewear: sunglasses for men, women, children - Watches - Mountainwear in particular for ski and snowboard - Bags and accessories: underwear, socks, belts, caps.. The group was developed on a business model based on the total subcontracting of the manufacturing of products, essentially in Asia area. This model allows the company to focus on the key points of its activity: the design, the development, the sales and marketing, the distribution and the communication around the main brand. The distribution policy of SURFWEAR is based on few franchisee retailers and many multibrands shops among which some shops specialised in slipping sport equipments (surf, snowboards, skies...). Products are also distributed using remote sale companies as in France La Redoute, 3 Suisses...SURFWEAR has a logistical platform to which they receive all the products from subcontractors and from which they deliver all the orders to their customers. The policy of the brand is based on a young, dynamic, and freedom image. The company uses to sponsor also competitions of surf all around the world to promote the brand. The technological innovations are the main “engine” of the brand evolution through a continuous programme called “the search”. The turnover of the European part of the group is around 130M€ with a growth of 9% per year. It employs around 260 people grouped in eight main departments: the style, the development, the procurement, the marketing, the commercial, the logistical platform, the IT and the product manager departments. The reasons of changes and objectives of the study SURFWEAR is evolving in the very versatile environment of the fashion products continuously in movement. This is why they need to control their strategy to react quickly to stay competitive. From one collection to another, all the performance can be reconsidered according to the customer vision of the brand evolution and the reactions of the opinion leaders (sportives, magazine’s editorialists...). Moreover, their business models allow the company to minimize the production risks but leads to a strong dependence to the subcontractors effectiveness. In this context, SURFWEAR must improve continuously its supply chain and must reach the following objectives: - To have the best service rate (number of orders delivered on time) in order to satisfy its customers - To guarantee the best time to market for its collection in order to meet the seasonality of its products, - To optimise the capacity of the logistical platform in order to meet the commercial objectives. The company processes and decisions are established according to a master calendar of the collection which fixes the main key dates for the milestones: end of the design, choice of the models, end of the corrections on the models according to the customer feedback, beginning of the production. Their previous Performance Measurement System was developed several years ago when the company was focused only on material for surf (board and accessories and Surfwear) and when most of the production was done in Europe.
With the increasing of product families and of subcontractors, the PMS was not adapted: - In terms of indicators: few indicators for such an organisation, - Many strategic indicators, no tactical and few operational ones, - Many indicators focused on distribution and few on design, - Difficulties to get the information to build indicators. So, a first study has allowed detecting the following points to improve which have led to the objectives of the study: Design department: Most of the samples of the collection (new products) are available in late in comparison to the master calendar. Sourcing department: Collection is incomplete at the beginning of the season and procurements are launched in late. Sales department: Many sales are wrong due to the non availability of the whole collection, the order book is not enough smoothed. Logistic platform: Several delays on the turnover, claims from customers for deliveries in advance or in late. So, the objectives of Performance Indicator reports are presented in the figure 1 hereafter as defined by the company itself: Follow up of collection design
Objectives of PI reports
To visualise advancement of key steps of design activities and to compare to the master calendar scheduling to: ‐Anticipate delays in business processes ‐‐ Relaunch actors of business processes
Follow up of material consumption
To visualise the progress of model number and the consumption of material by model in particular for surfware to: ‐ Measure the achievement of objectives in terms of material and number of developed models
Follow up of collection launching
To visulaise the delivery status of products to retailers and measure criticity of delays for particular models to: ‐Visualise coherence between inputs and outputs of the platforms ‐Visualise delays of orders from suppliers and relaunch suppliers
Foolow up of supplier’s deliveries
To evaluate suppliers according to the quality of their deliveries in terms of product quality, quantity and date and evaluate suppliers according to cost and margin of products
Figure 1: Objectives of PI reports for each department All the objectives of the PMS are not described in this figure 1 as for instance the resource management or the commercial follow up because these aspects were already taken into account in the previous PMS. The objectives of figure 1 are mainly the new ones which must be added to the “classical ones”. The previous PMS was supported by Business Object software. It was decided to keep this tool for the next PMS because the company did not want to change all things at the same
time. The structure of the information system dedicated to the PMS is given in the figure 2 below:
AS/400 DB2 (Iris Conf ection)
Commercial and accounting data
ORACLE (Quest)
Dex
ORACLE 9i « TEMPON »
Consolidation of data
Dex
ORACLE 9i « BASE »
Business Process data
ORACLE 9i « REFERBO »
BusinessObjects
referential
Reporting tool : BusinessObjects
Product design and development data
Figure 2: The structure of information system dedicated to PMS The feeding of datawarehouse “TEMPON” is done through the ETL (Extract Transform Load) tool Data Exchanger (Dex) and the two databases: DB2 for the commercial and accounting data and ORACLE Quest for the design and development data. When the data are cleaned, Dex tool transfer them in the second datawarehouse BASE from which Business Object collect data and process them to obtain performance indicators. Within this framework, the company decided to use ECOGRAI to define and implement a new performance indicator system to measure the company performance related to the new objectives above. Indeed, this choice was guided by the wish of the company to have a customised PMS for each company employee, to insist on the action means to reach objectives and not only on performance, and to have a coherence analysis between performance indicators in order to avoid a system in which decision makers “don’t go in the same direction”. Moreover, the company wanted to deploy a method which involves future performance indicators users from the beginning in order to install a real performance culture in this company which had a rapid growth. So, in the next part, ECOGRAI is presented insisting on its originality to define performance indicators not only based on objectives to reach but also on the action means (decision variables) available for the decision making in order to ensure that the decision makers have the means to make evolve the performance.
ECOGRAI Method ECOGRAI is a method to design and to implement performances indicators systems for organizations (Bitton 1990) (Ducq 2005 et al.). This method has been developed for more than twenty year at University Bordeaux 1 and evolves continuously according to the
evolution of enterprise performance measurement requirements in more and more complex companies and supply chains. This method is applied with the involvement of the decision makers of the production management system. It exists two main steps in this method: design and implementation. The results of the design step are a coherent set of specification sheets describing each Performances Indicator (indicators, concerned actors, required information and processing...). The main characteristics of ECOGRAI method are: - a logical process of analysis / design using a top-down approach, and allowing to decompose the objectives of the strategic levels into objectives for operational levels, - the use of tools and graphic supports: GRAI grids, actigrams, splitting up diagrams, coherence panels, specification sheets, - a coherent distribution of performances indicators covering the various functions and the various decision levels (strategic / tactical / operational), - the search of a limited number of performances indicators by an original and integrated approach (figure 4) that we will detail below and that allows to define a limited and coherent set of indicators. In fact, the originality of the ECOGRAI method is not in the definition of Performances Indicators, but the search of action means (decision variables) on which decision makers can act to reach their objectives (figure 3). This allow to ensure that people who has to make the performance evolve has the required tools to do so.
OBJECTIVES 1
1
DECISION VARIABLES
PERFORMANCE INDICATORS
1
Figure 3: The performance controllability principle of the ECOGRAI approach This approach allows to define a real customised performance measurement system with multi criteria performance indicators for each people of the enterprise. The logical structured approach of the method is decomposed into six phases (figure 4). The first phase consists in the modelling the control structure (decision system) and the controlled structure: the business system (physical transformation system) of the Enterprise or the studied domain. The three following phases aim at identifying the basic elements which are required: the coherent objectives (performance to achieve by the controlled activity of the physical system) and the decision variables to reach this performance. The fourth phase consists in identifying the performances indicators (actual performance), the fifth in designing the information system to build the Performances Indicators, and the sixth in implementing it inside the Enterprise Information System. The different phases are detailed through the case study of the following paragraphs.
CONTROLLED STRUCTURE : ACTIGRAMS
CONTROL STRUCTURE : GRAI GRID F1 F 2 F3
A2 A1
PHASE 1
A3
...
Fn
Stratégic
MODELLING OF THE CONTROL AND THE CONTROLLED STRUCTURE – GRAI GRIDS AND ACTIGRAMS
Tactical Opérational
COHERENT OBJECTIVES
PHASE 2
IDENTIFICATION OF OBJECTIVES AND COHERENCE ANALYSIS BY PERFORMANCE AGGREGATION
PHASE 3
COHERENT DECISION VARIABLES
IDENTIFICATION OF DECISION VARIABLES (DV) AND ANALYSIS OF CONFLICS BETWEEN DV
COHERENT PERFORMANCE INDICATORS
PHASE 4
DEFINITION OF PERFORMANCE INDICATORS AND INTERNAL COHERENCE ANALYSIS
PHASE 5
DESIGN OF PI INFORMATION SYSTEM
PHASE 6
INTEGRATION OF PI INFORMATION SYSTEM IN ENTERPRISE INFORMATION SYSTEM USING DECISIONAL TOOL
Figure 4: the structured approach of ECOGRAI In its last developments, ECOGRAI is including the coherence analysis of objectives as well as developments to ensure interoperability of decision support systems. This coherence analysis is based on coherence decomposition diagrams for objectives and on coherence panels between objectives-decision variables-performance indicators of eacg decision maker.
Results of the study The results of the study are presented in two parts. The first part concerns the strict application of ECOGRAI to define coherent set of performance indicators. The second part concerns the implementation of the performance indicators in the IT system of the company. GRAI Grid and definition of Performance indicators. This part presents the decision model of the company in Europe. This decision model is represented using GRAI Grid as shown in figure 5 below. External information
To sell
To perform marketing and CRM
To design and develop
To manage products
To plan
To manage resources
To distribute
Internal information
Market conditions
To define sell policy / To review the prices of carry over models
To define the marketing strategy and the targeted customers / to analyse the customer
To define product features / To define image of the brand
To define strategic suppliers and critical products
Business Planning
Investment planning in new technologies and machines
To define the devely conditions by geographical areas and delivery manner
Turnover of year n-1
Fashion trends
Global sale forecasts
To manage the customer data / To define advertising budget / To define the future fashion trends
Definition of new styles / to refine the suppliers / To update DB2 data base
To negociate the prices and to reserve quantities
Master callendar / Budget
Employment planning
To negociate contracts with transport companies
Calendar and budget of year n-1
Information on consumer needs
To fix the prices of the new models / sale forecast
To valid the models to produce
Development of the collection / To define the suppliers for the collection
To define orders (global quantities)
Master planning / Make or buy decisions
To smooth the capacity between product famillies
To evaluate the transportation companies
Inventory of regular models
Info from shops
To organise the regular shop visits
To collect ideas from trend people
Bi-monthly planning for design modifications (custom made)
To define the delivery window for material / To order / To relaunch suppliers and S/C
Platform load planning / Weekly planning
To allocate employees by product familly
To look for exeptional transportation companies
Status of the resources
Consumer claims and urgent orders
To valid the order of the consumer/ To manage the consumer order
To manage consumer claims and phone calls
Scheduling of custom made design
To define urgent purchasing / To order
Scheduling of deliveries
To realocate employees for the week
To define a bi-monthly round planning / To manage urgent deliveries
Status of the resources
H = 3 ans P = 6 mois
10 H = 1 an P = 1 mois
20 H = 6 mois P = 1 mois
25 H = 2 mois P = 1 semaine
30 H = 2 semaines P = 1 jour
40
Figure 5: The decision model represented using GRAI Grid
The GRAI Grid is decomposed using two criteria. The columns represent the functions to manage in the companies and the related decisions inside each function. The lines represent decision levels, i.e. levels at which decisions are made. Each decision level is defined by a horizon (on which duration the decisions are made) and a period (after which lead time decisions are reconsidered). For instance, this company is elaborating its master calendar (in the column planning) for one year. The master calendar is reviewed every month according to the problems which occur in the company. The blue/bold arrows are represented hierarchical links between decisions and the red/normal arrows are representing exchange of information. For instance the master calendar is made in respect to the business planning and is using the master calendar of the previous year. Then, the master planning and make or buy decisions are done with respect of this master calendar which becomes then a frame for this decision. In our approach, the decision model is very crucial because it represents the philosophy of decision making in the company and it is well recognised the performance measurement is only required to make the best decisions. Without decision power, no measure is necessary. Moreover, the decision model obliges to measure and manage performance at all decision levels: strategic at long term on the top of the GRAI Grid (level 10), tactical at the medium term in the intermediate levels (levels 20 and 25) and operational at short term (levels 30 and 40). This allows having a coherent dispatching of performance indicators. Moreover, performance measurement is balanced for all the functions which is close to the philosophy of balanced score card. So, based on the thirty five decisions presented in the GRAI Grid, a lot of performance indicators were proposed for implementation in order to verify the achievement of these decision objectives and efficiency of their decision variables. Some examples are presented in figure 6 below. External information
To sell
To perform marketing and CRM
To design and develop
To manage products
To plan
To manage resources
To distribute
Internal information
Market conditions
Prices of carry over models
Number of customer segments - Cost of marketing actions
Customer satisfaction related to image
Number of suppliers - % in Asia and Europe
Total turnover Turnover by product familly
% of cash invested in machines for prototypes by year
% of products delivered by plane, ship, trucks
Turnover of year n-1
Fashion trends
Global sale forecasts
Number of new trends in the collections Number and % of new ideas from retailers
Number and % of new style in the collection and recurent products
Price by supplier for similar products - Total and % Quantity by supplier
Customer service rate: % of order delivered to retailers on time
% of increasing of employees dispatching per departement
Number of contracts w ith transportation companies - Cost by kilometer - % of orders in late due to transport
Calendar and budget of year n-1
Information on consumer needs
Prices and % of evolution for the new models / sale forecast
Number of iterations before validation of each model
Number of new suppliers by collection Quantity of material used for each model
Number and % of global orders
Turnover delivered on the period - % of turnover delivered on the period Volume delivered on the period
% of employees allocated to each product familly
Logistic cost Difference between inputs and outputs
Inventory of regular models
Info from shops
Number of regular shop visits
Number of models available on time for exhibitions
Number of modifications - % of models designed in late
Number and % of order on time for each supplier -
% of overload and underload per period
evolution of employee allocation per product familly per week
Number and % of exeptional transportaion contracts - Number of late deliveries due to transport
Status of the resources
Consumer claims and urgent orders
Number and % of deliveries in late on the period
Number of customer and retailer claims
Number of short term modifications in the models due to customer feed back
Number of urgent order to extra suppliers due to delays
% on goods delivered on time Comparison inputs/outputs of each platform
Number of temporary employees allocated by product familly
Number of revision of the round planning - Number and % of urgent deliveries
Status of the resources
H = 3 ans P = 6 mois
10 H = 1 an P = 1 mois
20 H = 6 mois P = 1 mois
25 H = 2 mois P = 1 semaine
30 H = 2 semaines P = 1 jour
40
Figure 6: Some performance indicators selected for each decision of the GRAI Grid In the figure 6, the performance indicators have been placed in the decision centre to which they are related. They composed the PMS implemented in the company excepted those that the company does not want to disseminate in such a paper.
The PI’s are coloured according to the objectives of the PI reports of figure 1 they aim to achieve. As mentioned previously, many PI’s are not directly connected to the objectives of figure 1 because either they are related to classical objectives of a PMS as the management of resources, either their role is mainly to ensure the coherence between PI’s, in terms of aggregation mechanisms. They ensure the integration of functions through the performance achievement. In order to understand the relevancy of this set of performance indicators, we are going to detail three PI’s: “Turnover delivered on the period - % of turnover delivered on the period – Volume delivered on the period”. These PI’s are at the level 25 (Horizon=6 months and Period=1 month). So, they are considered on six months and updated month by month. The coherence panel for this indicator is presented below:
OBJECTIVES
Fonction: To plan
Decision Centre: Master Planning
O1: To maximise turnover on bank and minise stock value O2 : To reach a service rate to retailers of 80%
**
*
IP1 : Turnover delivered on the period
PERFORMANCE INDICATORS DECISION VARIABLES
INTERNAL COHERENCE ANALYSIS
VD1: Choice of the platform
*
VD2: Choice of supplier
**
**
**
IP2 : % of turnover delivered on the period **
**
*
**
IP3 : Volume delivered on the period
**
**
strong link (**) / weak link (*) / no link ( )
Figure 7: The coherence panel of master planning performance indicator The coherence panel of figure 7 shows that the defined indicators are coherent with objectives of decision makers and with their action means. Indeed, they can choose from which platform the retailers will be delivered and in case of problems of late deliveries, they can choose another supplier in emergency. An example of performance indicator report is presented in the figure 8 below. This report shows the various indicators allowing to measure the progress of deliveries in terms of quantities and in terms of turnover for each period.
2008
Year Month Value
June
July
August
September
October
November
Ordered Turnover Delivered Turnover % Delivered % Delivered (cumulated) Available % Available Allocated Pre Packed Ready to be delivered Remainder to Deliver (RtD) Losses (remainder‐available) Losses (cumulated) Rate of cover (%avail/RtD) Ordered Quantity DeliveredQuantity % Delivered % Delivered (cumulated) Delivered(cumulated) Quantity available for delivery % available for delivery Allocated quantity Pre Packed quantity Prepared quantity Remainder to Deliver (RtD) Rate of cover (% avail/RtD)
Figure 8: Performance report allowing to measure the status of deliveries in terms of volume and turnover
Conclusion: Impact of PMS on the enterprise and lessons learned The first impact of the new PMS was for the company to have a clear view of their performances in terms of customer service rate, delays in deliveries and reliability of the numerous suppliers. Moreover, the impact of enterprise modelling to design performance indicators was to capture the knowledge of the enterprise and to have a common language and a common culture between the various heterogeneous functions and the various levels of the hierarchy. This was very important for this enterprise because the subcontracting was very difficult to master in terms of quantity and quality with respect to the collection spirit. One of the main point to solve was to collect data from various platform, with the existing decision system tool (mainly ETL+Datawarehouse+Business Object) and to anticipate the data of the suppliers. Then for the implementation, this was necessary to anticipate interoperability problems between existing databases. The last impact concerned the appropriation of the models and of the performance indicators by the employees because these were defined by the actors of the system and because they were customised and not extracted from a list. Finally, this study demonstrated the applicability of enterprise modelling techniques and of ECOGRAI to the textile industry. Most of the models obtained are reusable in other fashion industry as footwear industry in order to master the collection and the production of versatile collections.
The youthness of the management team, dynamic and open to changes was also an important factor of success in this study even if this had some disadvantages as the continuous changes in their opinion due to inexperience of such performance indicators. Moreover, all the managers were not available at the same time due to their numerous travels around the world and this led to difficulties to converge towards common agreed solutions. References Bititci U.S., Carrie A.S., Mcdevitt L. (1997) “Integrated Performance Measurement System: a development guide” – International Journal of Operations & Production Management, vol 17, n° 5-6, 1997, pp 522-534. Bitton M. (1990) « ECOGRAI : Méthode de conception et d'implantation de systèmes de mesure de performance pour organisations industrielles » - Thèse de doctorat en Automatique - Université Bordeaux I - Septembre 1990 Ducq Y., Vallespir B., Doumeingts G. (2001) “Coherence analysis method for production system by performance aggregation” - International journal of Production Economics – Number 69 – 2001 – pp 23-37 Ducq Y., Vallespir B. (2003) “Definition and aggregation of a Performance Measurement System in three Aeronautical workshops using GRAIPerf Method” in proceedings of the 3rd International Workshop on Performance Measurement” – Bergamo – June 19-20th 2003 Ducq Y., Vallespir B. (2005) “Definition and aggregation of a Performance Measurement System in three Aeronautical workshops using the ECOGRAI Method” - International Journal of Production Planning and Control, vol. 16, n° 2, March 2005, pp 163-177. Franco M., Bourne M. (2003) “An examination of the literature relating to issues affecting how companies manage through measures” in proceedings of the 3rd International Workshop on Performance Measurement” – Bergamo – June 19-20th 2003 Ghalayini A.M., Noble J.S., Crowe T.J. (1997) “An integrated dynamic performance measurement system for improving manufacturing competitiveness” – International Journal of Production Economics, vol. 48, 1997, pp 207-225. Kaplan R.S., Norton D.P. (1996) “Translating strategy into action : The Balanced Scorecard” – Harvard Business School Press – Boston, Mass 1996 Neely A., Adams C., Kennerley M. (2002) “The performance Prism – The scorecard for measuring and managing Business Success” – Edition Prentice Hall – 2002 – 394p. Ravelomanantsoa M., Ducq Y., Vallespir B (2006) “A generic framework for performance indicator system methods” - The 5th International Conference on Theory and Practice in Performance Measurement and Management – London - UK – 25-28 July 2006
